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In [1]:
import pandas as pd
import urllib
import numpy as np
import urllib.request
import re
from textblob import TextBlob
%run lib.py
In [2]:
#name="Legally%20Blonde"
#name="aboutmary"
#name="10Things"
name="magnolia"
#name="Friday%20The%2013th"
#name="Ghost%20Ship"
#name="Juno"
#name="Reservoir+Dogs"
#name="shawshank"
#name="Sixth%20Sense,%20The"
#name="sunset_bld_3_21_49"
#name="Titanic"
#name="toy_story"
#name="trainspotting"
#name="transformers"
#name="the-truman-show_shooting"
#name="batman_production"
In [3]:
ext="html"
txtfiles=["Ghost%20Ship", "Legally%20Blonde", "Friday%20The%2013th", "Juno", "Reservoir+Dogs", "Sixth%20Sense,%20The", "Titanic"]
if name in txtfiles:
    ext="txt"
fp = urllib.request.urlopen("http://www.dailyscript.com/scripts/"+name+"."+ext)
mybytes = fp.read()

mystr = mybytes.decode("utf8", "ignore")
fp.close()
liston=mystr.split("\n")
liston=[s.replace('\r', '') for s in liston]
liston=[re.sub('<[^<]+?>', '', text) for text in liston]
In [4]:
if name=="shawshank":
    liston=[i.replace("\t", "    ") for i in liston]
In [5]:
char=""
script=[]
charintro='                                 '
endofdialogue='          '
dialoguepre='                    '
newscenepre='          '
charintro=''
endofdialogue=''
dialoguepre=''
newscenepre=''
i=45
print("Characters")
i, charintro=nextbigchunk(liston, i)
print("Adverbs")
i, adverb=nextbigchunk(liston, i, adverbs=True)
print("Dialogues")
i, dialoguepre=nextbigchunk(liston, i)
print("New Scene:")
i, newscenepre=nextbigchunk(liston, i)

if newscenepre=="X":
    i=100
    i, newscenepre=nextbigchunk(liston, i)
    if name=="aboutmary":
        newscenepre=" ".join(["" for i in range(56)])
    if len(newscenepre)==len(charintro):
        newscenepre="X"
    

endofdialogue=newscenepre
    

scene=1
for s in liston:
    if s[0:len(charintro)]==charintro and s[len(charintro)]!=" " and s.strip()[0]!="(" and s.strip()[len(s.strip())-1]!=")":
        #print("Charatcer*****")
        char=s[len(charintro):]
        new=dict()
        new['char']=char.strip()
        new['dialogue']=""
        new['scene']=scene
        new['adverb']=""
    if s==endofdialogue or s.replace(" ", "")=="":
        if char!="":
            char=""
            script.append(new)
    if char!="" and s[0:len(dialoguepre)]==dialoguepre and s[len(dialoguepre)]!=" ":
        #print("Dialogue******")
        if new['dialogue']!="":
            new['dialogue']=new['dialogue']+" "
        new['dialogue']=new['dialogue']+s[len(dialoguepre):]
    if char!="" and ((s[0:len(adverb)]==adverb and s[len(adverb)]!=" ") or (len(s)>1 and s.strip()[0]=="(" and s.strip()[len(s.strip())-1]==")" )):
        if new['adverb']!="":
            new['adverb']=new['adverb']+" "
        new['adverb']=new['adverb']+s[len(adverb):]
    if s[0:len(newscenepre)]==newscenepre and len(s)>len(newscenepre) and ( s.isupper()) and s[len(newscenepre)]!=" ":
        scene=scene+1
Characters
                                magnolia
                                NARRATOR
                                NARRATOR
                                NARRATOR
                                NARRATOR
                                NARRATOR
Adverbs
Dialogues
                      In the New York Herald, November 26,
                      year 1911, there is an account of the
                      hanging of three men --
                      ...they died for the murder of
                      Sir Edmund William Godfrey --
                      -- Husband, Father, Pharmacist and all
New Scene:
     a P.T. Anderson picture                             11/10/98
     a Joanne Sellar/Ghoulardi Film Company production
     
     
     
     
In [6]:
pd.DataFrame(script).to_csv(name+'.csv', index=None)
pd.DataFrame(script)
Out[6]:
adverb char dialogue scene
0 magnolia 1
1 NARRATOR In the New York Herald, November 26, year 1911... 2
2 NARRATOR ...they died for the murder of Sir Edmund Will... 2
3 NARRATOR -- Husband, Father, Pharmacist and all around ... 2
4 NARRATOR Greenberry Hill, London. Population as listed. 3
5 NARRATOR He was murdered by three vagrants whose motive... 5
6 NARRATOR ...Joseph Green..... 5
7 NARRATOR ...Stanley Berry.... 5
8 NARRATOR ...and Nigel Hill... 5
9 NARRATOR Green, Berry and Hill. 7
10 NARRATOR ...And I Would Like To Think This Was Only A M... 7
11 NARRATOR As reported in the Reno Gazzette, June of 1983... 9
12 NARRATOR --- the water that it took to contain the fire -- 10
13 NARRATOR -- and a scuba diver named Delmer Darion. 12
14 NARRATOR Employee of the Peppermill Hotel and Casino, R... 15
15 NARRATOR -- well liked and well regarded as a physical,... 16
16 NARRATOR -- as reported by the coroner, Delmer died of ... 21
17 NARRATOR ...volunteer firefighter, estranged father of ... 24
18 NARRATOR -- added to this, Mr. Hansen's tortured life m... 26
19 CRAIG HANSEN ...oh God...fuck...I'm sorry...I'm sorry... 27
20 NARRATOR The weight of the guilt and the measure of coi... 27
21 CRAIG HANSEN ...forgive me... 27
22 NARRATOR And I Am Trying To Think This Was All Only A M... 29
23 NARRATOR The tale told at a 1961 awards dinner for the ... 32
24 NARRATOR Seventeen year old Sydney Barringer. In the ci... 33
25 NARRATOR The coroner ruled that the unsuccessful suicid... 33
26 NARRATOR The suicide was confirmed by a note, left in t... 34
27 NARRATOR At the same time young Sydney stood on the le... 35
28 NARRATOR The neighbors heard, as they usually did, the... 36
29 NARRATOR -- and it was not uncommon for them to threat... 37
... ... ... ... ...
1493 DIXON We gotta get his money so we can get outta her... 382
1494 WORM That idea is over now. We're not gonna do tha... 382
1495 (to Stanley) DIXON DADDY, FUCK, DADDY, DON'T GET MAD AT ME. DON'T... 382
1496 WORM I'm not mad, son, I will not be mad at you an... 382
1497 DIXON DAD. 382
1498 DIXON I - just - thought - that - I - didn't want - ... 382
1499 WORM It's ok, boy. 382
1500 MUSIC/KERMIT THE FROG "It's not that easy bein' green... Having to s... 383
1501 DONNIE My teeff...my teeef.... 385
1502 JIM KURRING YOU'RE OK...you're gonna be ok.... 385
1503 NARRATOR And there is the account of the hanging of thr... 390
1504 NARRATOR There are stories of coincidence and chance an... 391
1505 NARRATOR ...and we generally say, "Well if that was in... 392
1506 DOCTOR Are you with us? Linda? Is it Linda? 394
1507 NARRATOR Someone's so and so meet someone else's so and... 395
1508 NARRATOR And it is in the humble opinion of this narrat... 398
1509 STANLEY Dad...Dad. 399
1510 STANLEY You have to be nicer to me, Dad. 399
1511 RICK Go to bed. 399
1512 STANLEY I think that you have to be nicer to me. 399
1513 RICK Go to bed. 399
1514 NARRATOR ...and so it goes and so it goes and the book... 400
1515 MARCIE I killed him. I killed my husband. He hit my... 401
1516 DONNIE I know that I did a thtupid thing. Tho-thtupid... 402
1517 DONNIE I really do hath love to give, I juth don't kn... 402
1518 JIM KURRING ...these security systems can be a real joke. ... 403
1519 DONNIE ....ohh-thur-I-thur-thill.... 403
1520 JIM KURRING You guys make alotta money, huh? 403
1521 (beat) JIM KURRING ...alot of people think this is just a job tha... 405
1522 END. 406

1523 rows × 4 columns

In [7]:
magnolia=pd.read_csv(name+'.csv')
stopwords = getstopwords()
In [8]:
removedchars=["'S VOICE", "'S WHISPER VOICE", " GATOR"]
for s in removedchars:
    magnolia['char']=magnolia['char'].apply(lambda x: x.replace(s, ""))
i=0
scenes=dict()
for s in magnolia.iterrows():
    scenes[s[1]['scene']]=[]
for s in magnolia.iterrows():
    scenes[s[1]['scene']].append(s[1]['char'])
for s in magnolia.iterrows():
    scenes[s[1]['scene']]=list(set(scenes[s[1]['scene']]))
In [9]:
characters=[]
for s in scenes:
    for k in scenes[s]:
        characters.append(k)
characters=list(set(characters))
appearances=dict()
for s in characters:
    appearances[s]=0
for s in magnolia.iterrows():
    appearances[s[1]['char']]=appearances[s[1]['char']]+1
In [10]:
a=pd.DataFrame(appearances, index=[i for i in range(len(appearances))])
In [11]:
finalcharacters=[]
for s in pd.DataFrame(a.transpose()[0].sort_values(0, ascending=False))[0:10].iterrows():
    finalcharacters.append(s[0])
In [12]:
finalcharacters
file=open(name+"_nodes.csv", "w")
couplesappearances=dict()
for s in finalcharacters:
    file.write(";")
    file.write(s)
file.write("\n")
for s in finalcharacters:
    newlist=[]
    for f in finalcharacters:
        newlist.append(0)
        couplesappearances[f+"_"+s]=0
    j=0
    for f in finalcharacters:
        for p in scenes:
            if f in scenes[p] and s in scenes[p] and f!=s and finalcharacters.index(f)<finalcharacters.index(s): 
                long=len(magnolia[magnolia["scene"]==p])
                newlist[j]=newlist[j]+long
                couplesappearances[f+"_"+s]=couplesappearances[f+"_"+s]+long
        j=j+1
    file.write(s)
    for f in newlist:
        file.write(";")
        file.write(str(f))
    file.write("\n")
file.close()
In [13]:
a=pd.DataFrame(couplesappearances, index=[i for i in range(len(couplesappearances))])
finalcouples=[]
for s in pd.DataFrame(a.transpose()[0].sort_values(0, ascending=False))[0:4].iterrows():
    finalcouples.append(s[0])
In [14]:
file=open(name+"_finalcharacters.csv", "w")
for s in finalcharacters:
    file.write(s+"\n")
file.close()
file=open(name+"_finalcouples.csv", "w")
for s in finalcouples:
    file.write(s+"\n")
file.close()
In [15]:
importantchars=[]
for char in appearances:
    if appearances[char]>10:
        importantchars.append(char)
In [16]:
file=open(name+"_sentiment_overtime_individual.csv", "w")
file2=open(name+"_sentiment_overtime_individualminsmaxs.csv", "w")

for k in finalcharacters:
    print(k)
    dd=getdialogue(magnolia, k, k, scenes)
    dd=[str(d) for d in dd]
    polarities, subjectivities=getsentiment(dd)
    %matplotlib inline
    import matplotlib.pyplot as plt
    moveda=maverage(polarities, dd, .99)
    plt.plot(moveda)
    i=0
    for s in moveda:
        file.write(k+","+str(float(i)/len(moveda))+", "+str(s)+"\n")
        i=i+1
    plt.ylabel('polarities')
    plt.show()
    file2.write(k+"| MIN| "+dd[moveda.index(np.min(moveda))]+"\n")
    file2.write(k+"| MAX| "+dd[moveda.index(np.max(moveda))]+"\n")
    print("MIN: "+dd[moveda.index(np.min(moveda))])
    print("\n")
    print("MAX: "+dd[moveda.index(np.max(moveda))])
    
file.close()
file2.close()

file=open(name+"_sentiment_overtime_couples.csv", "w")
file2=open(name+"_sentiment_overtime_couplesminsmaxs.csv", "w")

for k in finalcouples:
    print(k)
    liston=k.split("_")
    dd=getdialogue(magnolia, liston[0], liston[1], scenes)
    dd=[str(d) for d in dd]
    polarities, subjectivities=getsentiment(dd)
    %matplotlib inline
    import matplotlib.pyplot as plt
    moveda=maverage(polarities, dd, .99)
    plt.plot(moveda)
    i=0
    for s in moveda:
        file.write(k+","+str(float(i)/len(moveda))+", "+str(s)+"\n")
        i=i+1
    plt.ylabel('polarities')
    plt.show()
    file2.write(k+"| MIN| "+dd[moveda.index(np.min(moveda))]+"\n")
    file2.write(k+"| MAX| "+dd[moveda.index(np.max(moveda))]+"\n")
    print("MIN: "+dd[moveda.index(np.min(moveda))])
    print("\n")
    print("MAX: "+dd[moveda.index(np.max(moveda))])
    
file.close()
file2.close()
JIM KURRING
MIN: You mind if I check things back here? 


MAX: YOU'RE OK...you're gonna be ok....
JIMMY
MIN: She went crazy.  She went crazy, Rose. 


MAX: Imagine you are attending a jam session of classical composers and they have  each done an arrangment of the classic  favorite, "Whispering."  Here are three  variations on the theme, as three classic  composer's might have written it -- you are to name the composer.  The First: 
CLAUDIA
MIN: I'm sorry. 


MAX: Did you ever go out with someone and just....lie....question after question, maybe you're trying to  make yourself look cool or better  than you are or whatever, or smarter  or cooler and you just -- not really lie, but maybe you just don't say everything --
FRANK
MIN: If you feel, made to feel like you need them, like -- like you can't live if you're without them or you need, what?  They're pussy?  They're love? Fuck that.  Self Sufficient, gents.  That's the truth. What you are -- we are -- you need them  for what?  To fucking make you a piece of snot rag?  A puppett?  huh?  Hear them bitch and moan? bitch and moan --  and we're taught one thing -- go the other way -- there is No Excuse I will give you, I'm not gonna apologize -- I'm not gonna  apologize for my NEED my DESIRE...my, the  things that I need as a man to feel comfortable... You understand?  You understand?  You need to say something, "my mommy hit me or  daddy hit me or didn't let me play soccer,  so now I make mistakes, cause a that -- something, so now I piss and shit on it and do this." Bullshit.  I'm sorry. ok. yeah. no. fuck.  go.  fuck. alright. go make a new mistake. maybe not, I dunno...fuck.... 


MAX: I wouldn't want that to be misunderstood: My enrollment was totally unoffical because I was, sadly, unable to afford tuition up  there.  But there were three wonderful men who were kind enough to let me sit in on their classes, and they're names are:  Macready, Horn and Langtree among others. I was completely independent financially, and like I said: One Sad Sack A Shit.  So what we're looking at here is a true rags to riches story and I think that's  what most people respond to in "Seduce," And At The End Of The Day? Hey -- it may not  even be about picking up chicks and sticking your cock in it -- it's about finding What You Can Be In This World.  Defining It.  Controling It and  saying: I will take what is mine.  You just happen  to get a blow job out of it, then hey-what-the-fuck- why-not?  he.he.he.
PHIL
MIN: You wanna call him on the phone? We can call him, I can dial the  phone if you can remember the number -- 


MAX: Thank you, Chad, and good luck to you and your mother -- 
STANLEY
MIN: I think that you have to be nicer to me.


MAX: I'm fine. I'm fine, I just wanna keep playing --
DONNIE
MIN: My teeff...my teeef....


MAX: My name is Donnie Smith and I have lot's of love to give. 
EARL
MIN: No, no, the grade...the grade that you're in? 


MAX: "...it's not going to stop 'till you wise up..."
LINDA
MIN: listen...listen to me now, Phil:  I'm sorry, sorry I slapped your face.  ...because I don't know what I'm doing... ...I don't know how to do this, y'know?  You understand?  y'know?  I...I'm...I do things  and I fuck up and I fucked up....forgive me, ok? Can you just...


MAX: I'm listening.  I'm getting better. 
NARRATOR
MIN: -- added to this, Mr. Hansen's tortured life met before with Delmer Darion just two nights previous --


MAX: So Fay Barringer was charged with the  murder of her son and Sydney Barringer  noted as an accomplice in his own death...
JIM KURRING_CLAUDIA
MIN: You mind if I check things back here? 


MAX: ok. 
JIMMY_STANLEY
MIN: I don't mean to cry, I'm sorry. 


MAX: Imagine you are attending a jam session of classical composers and they have  each done an arrangment of the classic  favorite, "Whispering."  Here are three  variations on the theme, as three classic  composer's might have written it -- you are to name the composer.  The First: 
PHIL_EARL
MIN: -- it's not him. it's not him. He's the fuckin' asshole...Phil..c'mere... 


MAX: ...ah...maybe...yeah...she's a good one... 
FRANK_PHIL
MIN: When they put me on hold, to  talk to you...they play the tapes.  I mean: I'd seen the commercials and heard about you, but I'd never heard the tapes ....


MAX: I just...he was...but I gave him,  I just had to give him a small dose of  liquid morphine.  He hasn't been able to swallow the morphine pills so we now,  I just had to go to the liquid morphine... For the pain, you understand? 
In [17]:
for key, val in scenes.items():
    for s in scenes[key]:
        new="INSCENE_"+scenes[key][0]
        scenes[key].remove(scenes[key][0])
        scenes[key].append(new)
In [18]:
magnolia.dropna(subset=['dialogue'])
1
Out[18]:
1
In [19]:
baskets=[]
spchars=["\"", "'", ".", ",", "-"]
attributes=["?", "!"]
for s in magnolia.iterrows():
    if type(s[1]['dialogue'])!=float and  len(s[1]['dialogue'])>0:
        new=[]
        for k in scenes[s[1]['scene']]:
            new.append(k)
        new.append("SPEAKING_"+s[1]['char'])
        for k in s[1]['dialogue'].split(" "):
            ko=k
            for t in spchars:
                ko=ko.replace(t, "")
            for t in attributes:
                if ko.find(t)>=0:
                    new.append(t)
                    ko=ko.replace(t, "")
            if len(ko)>0:
                new.append(ko.lower())
        new=list(set(new))
        baskets.append(new)
In [20]:
baskets2=[]
basketslist=[]
for k in baskets:
    new=dict()
    new2=[]
    for t in k:
        if t not in stopwords:
            new[t]=1
            new2.append(t)
    baskets2.append(new)
    basketslist.append(new2)
In [21]:
baskets2=pd.DataFrame(baskets2)
from mlxtend.frequent_patterns import apriori
from mlxtend.frequent_patterns import association_rules
baskets2=baskets2.fillna(0)
baskets2.to_csv(name+'_basket.csv')
In [22]:
frequent_itemsets = apriori(baskets2, min_support=5/len(baskets2), use_colnames=True)
rules = association_rules(frequent_itemsets, metric="lift", min_threshold=1)
In [23]:
rules['one_lower']=[int(alllower(i) or alllower(j)) for i, j in zip(rules['antecedants'], rules['consequents'])]
In [24]:
rules['both_lower']=[int(alllower(i) and alllower(j)) for i, j in zip(rules['antecedants'], rules['consequents'])]
In [25]:
rules.to_csv(name+'_rules.csv', index=None)

Analisis de Sentimiento (Pelicula & Personaje)

Score por Pelicula

Titulo
.
RESERVOIR DOGS
Numero de Palabras/Tokens en el texto original
Palabras Distintas
1650
Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 3.774306 11.6%
Porcentaje de Palabras encontradas por tipo de sentimiento (bing) 11.9%
sentiment Porcentaje
negative 62.8%
positive 37.2%
Porcentaje de Palabras encontradas por tipo de sentimiento (nrc) 18.7%
sentiment Porcentaje
negative 19.7%
positive 13.5%
fear 10.8%
trust 10.1%
sadness 9.9%
anger 9.6%
anticipation 8.0%
disgust 7.6%
joy 6.0%
surprise 4.8%
Porcentaje de Palabras encontradas por tipo de sentimiento (loughran) 4.67%
sentiment Porcentaje
negative 52.2%
uncertainty 24.6%
positive 19.7%
litigious 3.0%
constraining 0.5%

Score por Personaje

[1] “Analisis de Sentimientos del Personaje: MR. WHITE” [1] “Numero total de Palabras Unicas en el texto: 589”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 3.705556 14.4%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 13.2%
sentiment Porcentaje
negative 67.1%
positive 32.9%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 18.7%
sentiment Porcentaje
negative 22.2%
fear 13.4%
anger 11.7%
positive 11.1%
sadness 10.5%
disgust 8.2%
trust 8.0%
anticipation 6.4%
surprise 4.9%
joy 3.5%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 5.09%
sentiment Porcentaje
negative 50%
positive 24%
uncertainty 24%
litigious 2%

[1] “Analisis de Sentimientos del Personaje: MR. PINK” [1] “Numero total de Palabras Unicas en el texto: 655”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 3.723926 10.8%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 9.77%
sentiment Porcentaje
negative 59.6%
positive 40.4%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 16.5%
sentiment Porcentaje
negative 17.0%
positive 14.6%
fear 12.3%
sadness 10.3%
trust 10.3%
anger 8.8%
anticipation 8.6%
joy 7.1%
disgust 5.8%
surprise 5.2%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 3.97%
sentiment Porcentaje
negative 45.1%
uncertainty 35.3%
positive 19.6%

[1] “Analisis de Sentimientos del Personaje: JOE” [1] “Numero total de Palabras Unicas en el texto: 513”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 4 11.5%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 10.3%
sentiment Porcentaje
negative 58.7%
positive 41.3%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 15.8%
sentiment Porcentaje
negative 19.7%
positive 13.1%
anticipation 11.8%
sadness 10.2%
trust 10.2%
fear 7.5%
joy 7.5%
disgust 7.2%
anger 6.9%
surprise 5.9%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 3.7%
sentiment Porcentaje
negative 59.4%
positive 15.6%
uncertainty 15.6%
litigious 9.4%

[1] “Analisis de Sentimientos del Personaje: MR. ORANGE” [1] “Numero total de Palabras Unicas en el texto: 490”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 3.814159 13.1%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 10.8%
sentiment Porcentaje
negative 71.3%
positive 28.7%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 13.5%
sentiment Porcentaje
negative 19.0%
positive 13.0%
fear 12.1%
trust 12.1%
sadness 11.1%
anger 9.5%
disgust 8.6%
anticipation 6.7%
joy 4.8%
surprise 3.2%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 3.27%
sentiment Porcentaje
negative 56%
uncertainty 32%
positive 12%

[1] “Analisis de Sentimientos del Personaje: EDDIE” [1] “Numero total de Palabras Unicas en el texto: 525”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 3.533333 10.7%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 9.33%
sentiment Porcentaje
negative 59.4%
positive 40.6%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 13.9%
sentiment Porcentaje
negative 18.3%
positive 14.9%
trust 11.6%
fear 10.0%
sadness 10.0%
anger 9.5%
anticipation 7.9%
disgust 7.1%
joy 6.6%
surprise 4.1%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 2.86%
sentiment Porcentaje
negative 55.6%
positive 16.7%
uncertainty 16.7%
constraining 5.6%
litigious 5.6%

[1] “Analisis de Sentimientos del Personaje: MR. BLONDE” [1] “Numero total de Palabras Unicas en el texto: 324”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 4.126984 13.6%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 10.5%
sentiment Porcentaje
negative 53.06%
positive 46.94%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 14.2%
sentiment Porcentaje
negative 22.0%
positive 12.8%
anger 11.0%
disgust 9.1%
fear 9.1%
trust 9.1%
joy 7.3%
surprise 7.3%
anticipation 6.7%
sadness 5.5%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 1.85%
sentiment Porcentaje
negative 50.0%
positive 33.3%
uncertainty 16.7%

[1] “Analisis de Sentimientos del Personaje: HOLDAWAY” [1] “Numero total de Palabras Unicas en el texto: 170”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 4.583333 11.2%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 9.41%
sentiment Porcentaje
positive 52.63%
negative 47.37%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 10.6%
sentiment Porcentaje
positive 22.2%
negative 20.4%
anger 11.1%
fear 11.1%
trust 9.3%
disgust 7.4%
sadness 7.4%
joy 5.6%
anticipation 3.7%
surprise 1.9%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 2.94%
sentiment Porcentaje
negative 42.9%
positive 42.9%
uncertainty 14.3%

[1] “Analisis de Sentimientos del Personaje: NICE GUY EDDIE” [1] “Numero total de Palabras Unicas en el texto: 163”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 4 8.59%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 6.75%
sentiment Porcentaje
negative 75%
positive 25%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 11.7%
sentiment Porcentaje
negative 21.3%
positive 14.9%
anticipation 10.6%
trust 10.6%
anger 8.5%
disgust 8.5%
joy 8.5%
sadness 8.5%
fear 4.3%
surprise 4.3%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 1.23%
sentiment Porcentaje
uncertainty 66.7%
litigious 33.3%

[1] “Analisis de Sentimientos del Personaje: MR. BROWN” [1] “Numero total de Palabras Unicas en el texto: 152”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 3.294118 14.5%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 13.2%
sentiment Porcentaje
negative 70%
positive 30%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 14.5%
sentiment Porcentaje
negative 21.5%
positive 16.9%
trust 12.3%
anticipation 9.2%
fear 9.2%
sadness 9.2%
anger 7.7%
disgust 6.2%
joy 6.2%
surprise 1.5%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 3.95%
sentiment Porcentaje
negative 83.3%
positive 16.7%

Score por Personaje en el tiempo

Top 10 Personajes

Dialogos cúspide por Top 10 Personajes: Reservoir+Dogs
Personaje Min_Max Dialogo
MR. WHITE MIN Hey, just cancel that shit right now! You’re hurt. You’re hurt really fucking bad, but you ain’t dying.
MR. WHITE MAX Piss on this turd, we’re outta here.
MR. PINK MIN Yeah, and that was a fucking miracle. But if they did get away, where the fuck are they?
MR. PINK MAX These ladies aren’t starvin’ to death. They make minimum wage. When I worked for minimum wage, I wasn’t lucky enough to have a job that society deemed tipworthy.
EDDIE MIN Holy shit, this guy’s all fucked up!
EDDIE MAX Ahh, now we’re getting down to it. It’s not just that he’s a cheap bastard -
JOE MIN No, she did it. She killed the cheatin’ wife, too.
JOE MAX Come in.
MR. ORANGE MIN I’m sorry, I’m so sorry.
MR. ORANGE MAX What?
MR. BLONDE MIN I said: “Are you gonna bark all day, dog, or are you gonna bite.”
MR. BLONDE MAX “Clowns to the left of me, Jokers to the right. Here I am, stuck in the middle with you.”
HOLDAWAY MIN It’s a scene. Memorize it.
HOLDAWAY MAX This better not be some Freddy joke.
MR. BROWN MIN My eyes! My eyes! I’m blind, I’m fucking blind!
MR. BROWN MAX I love this guy, he’s a madman, this guy.
MR. BLUE MIN These people are taxed on the tips they make. When you stiff ’em, you cost them money.
MR. BLUE MAX What’s something special, take ya in the kitchen and suck your dick?
MARVIN MIN What the fuck are they waiting for? That motherfucker cut off my ear! He slashed my face! I’m deformed!
MARVIN MAX I do. How do I look?

Top 4 Parejas

Dialogos cúspide por Top 4 Parejas: Reservoir+Dogs
Parejas Min_Max Dialogo
MR. WHITE_MR. PINK MIN Well, then, I’m afraid I’m gonna have to keep it.
MR. WHITE_MR. PINK MAX He’s right about the ear, it’s hacked off.
MR. PINK_EDDIE MIN Have you lost your fucking mind? Put your gun down!
MR. PINK_EDDIE MAX Ahh, now we’re getting down to it. It’s not just that he’s a cheap bastard -
MR. WHITE_EDDIE MIN Well, then, I’m afraid I’m gonna have to keep it.
MR. WHITE_EDDIE MAX Mr. Orange, why don’t you tell me what really happened?
MR. WHITE_MR. ORANGE MIN You don’t have any idea what you’re talking about. These people bust their ass. This is a hard job.
MR. WHITE_MR. ORANGE MAX Look, I don’t wanna be a fly in the ointment, but if help doesn’t come soon, I gotta see a doctor. I don’t give a fuck about jail, I just don’t wanna die.

Reglas de Asociación entre palabras (Market Basket)

Toda la pelicula

## [1] "Lift Promedio de las Reglas de Asociacion: 4.69485401156291"
## [1] "Desviación estandar del Lift de las Reglas de Asociacion: 2.06590961676948"
## [1] "Deciles del Lift : "
##       10%       20%       30%       40%       50%       60%       70% 
##  2.059233  2.723502  3.621324  4.766129  5.794118  5.794118  5.794118 
##       80%       90%      100% 
##  5.794118  5.794118 60.306122

Datos del Histograma: Lift Pelicula: RESERVOIR DOGS
Numero de Dialogos Lift Minimo Lift Maximo
1,412 -1 1
404,562 1 3
338,678 3 5
784,746 5 7
7,238 7 9
21,168 9 11
## [1] "Leverage Promedio de las Reglas de Asociacion: 0.0116943714177238"
## [1] "Desviación estandar del Leverage de las Reglas de Asociacion: 0.0145948327654564"
## [1] "Deciles del Leverage : "
##         10%         20%         30%         40%         50%         60% 
## 0.005247924 0.006542011 0.007000094 0.007000094 0.008400113 0.008649196 
##         70%         80%         90%        100% 
## 0.010032037 0.014000189 0.019147907 0.157867161

Datos del Histograma: Leverage pelicula: RESERVOIR DOGS
Numero de Dialogos Leverage Minimo Leverage Maximo
47,342 -0.0027 0.0027
698,272 0.0027 0.0082
503,520 0.0082 0.014
172,574 0.014 0.019
75,806 0.019 0.024
10,676 0.024 0.03

Top 10 Personajes

Top 4 Parejas

Analisis de Relaciones entre Personajes (Pagerank)

Pagerank: Reservoir Dogs.

Pagerank: Reservoir Dogs.